Advertisement

Introduction to Omics

  • Ewa Gubb
  • Rune Matthiesen
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 593)

Abstract

Exploiting the potential of omics for clinical diagnosis, prognosis, and therapeutic purposes has currently been receiving a lot of attention. In recent years, most of the effort has been put into demonstrating the possible clinical applications of the various omics fields. The cost-effectiveness analysis has been, so far, rather neglected. The cost of omics-derived applications is still very high, but future technological improvements are likely to overcome this problem.

In this chapter, we will give a general background of the main omics fields and try to provide some examples of the most successful applications of omics that might be used in clinical diagnosis and in a therapeutic context.

Key words

Clinical research bioinformatics omics machine learning diagnosis therapeutic 

References

  1. 1.
    Valkova N, Kultz D. (2006) Biochim Biophys Acta 1764:1007–1020. http://www.etymonline.com/index.php PubMedGoogle Scholar
  2. 2.
    Takatalo MS, Kouvonen P, Corthals G, Nyman TA, Ronnholm RH. (2006) Proteomics 6:3502–3508. http://en.wikipedia.org/wiki/-omics PubMedCrossRefGoogle Scholar
  3. 3.
    Kuska B. (1998) Beer, Bethesda, and biology: how “genomics” came into being. J Natl Cancer Inst 90:93.PubMedCrossRefGoogle Scholar
  4. 4.
    Zhang JF, He SM, Cai JJ, Cao XJ, Sun RX, Fu Y, Zeng R, Gao W. (2005) Genom Proteom Bioinformat 3:231–237.Google Scholar
  5. 5.
    Fiers W, Contreras R, Duerinck F, et al. (1976) Complete nucleotide sequence of bacteriophage MS2 RNA: primary and secondary structure of the replicase gene. Nature 260:500–507.PubMedCrossRefGoogle Scholar
  6. 6.
    Nowak R. (1995) Bacterial genome sequence bagged. Science 269:468–470.PubMedCrossRefGoogle Scholar
  7. 7.
    Fleischmann RD, Adams MD, White O, Clayton RA, Kirkness EF, Kerlavage AR, Bult CJ, Tomb JF, Dougherty BA, Merrick JM, et al. (1995) Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science 269:496–512.PubMedCrossRefGoogle Scholar
  8. 8.
    Sachidanandam R, et al. (2001) A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature 409:928–933.PubMedCrossRefGoogle Scholar
  9. 9.
    McPherson JD, Marra M, Hillier L, et al. (2001) A physical map of the human genome. Nature 409:934–941.PubMedCrossRefGoogle Scholar
  10. 10.
    Venter JC, Adams MD, Myers EW, et al. (2001) The sequence of the human genome. Science 291:1304–1351.PubMedCrossRefGoogle Scholar
  11. 11.
    Collins FS, Morgan M, Patrinos A. (2003) The Human Genome Project: lessons from large-scale biology. Science 300:286–290.PubMedCrossRefGoogle Scholar
  12. 12.
    Arnold J, Hilton N. (2003) Genome sequencing: Revelations from a bread mould. Nature 422:821–822.PubMedCrossRefGoogle Scholar
  13. 13.
    Watson JD, Crick FH. (1953) Molecular structure of nucleic acids; a structure for deoxyribose nucleic acid. Nature 171:737–738.PubMedCrossRefGoogle Scholar
  14. 14.
    Chong PK, Gan CS, Pham TK, Wright PC. (2006) J Proteome Res 5:1232–1240. http://www.ncbi.nlm.nih.gov/genomes/static/gpstat.html PubMedCrossRefGoogle Scholar
  15. 15.
    Ashburner M. (2007) Drosophila Genomes by the Baker’s Dozen. Genetics 177:1263–1268.PubMedGoogle Scholar
  16. 16.
    Gibson G. (2003) Microarray analysis: genome-scale hypothesis scanning. PLoS Biol 1:E15.PubMedCrossRefGoogle Scholar
  17. 17.
    Nguyen DH, D’Haeseleer P. (2006) Deciphering principles of transcription regulation in eukaryotic genomes. Mol Syst Biol 2:2006.0012.PubMedCrossRefGoogle Scholar
  18. 18.
    Landry CR, Oh J, Hartl DL, et al. (2006) Genome-wide scan reveals that genetic variation for transcriptional plasticity in yeast is biased towards multi-copy and dispensable genes. Gene 366:343–351.PubMedCrossRefGoogle Scholar
  19. 19.
    Stern S, Dror T, Stolovicki E, et al. (2007) Genome-wide transcriptional plasticity underlies cellular adaptation to novel challenge. Mol Syst Biol 3:106.PubMedCrossRefGoogle Scholar
  20. 20.
    Leban G, Bratko I, Petrovic U, et al. (2005) VizRank: finding informative data projections in functional genomics by machine learning. Bioinformatics 21:413–414.PubMedCrossRefGoogle Scholar
  21. 21.
    Wilkinson DJ. (2007) Bayesian methods in bioinformatics and computational systems biology. Brief Bioinform 8:109–116.PubMedCrossRefGoogle Scholar
  22. 22.
    Syvanen AC. (1994) Detection of point mutations in human genes by the solid-phase minisequencing method. Clin Chim Acta 226:225–236.PubMedCrossRefGoogle Scholar
  23. 23.
    Guo Z, Guilfoyle RA, Thiel AJ, et al. (1994) Direct fluorescence analysis of genetic polymorphisms by hybridization with oligonucleotide arrays on glass supports. Nucleic Acids Res 22:5456–5465.PubMedCrossRefGoogle Scholar
  24. 24.
    Pastinen T, Raitio M, Lindroos K, et al. (2000) A system for specific, high-throughput genotyping by allele-specific primer extension on microarrays. Genome Res 10:1031–1042.PubMedCrossRefGoogle Scholar
  25. 25.
    Hirschhorn JN, Sklar P, Lindblad-Toh K, et al. (2000) SBE-TAGS: an array-based method for efficient single-nucleotide polymorphism genotyping. Proc Natl Acad Sci USA 97:12164–12169.PubMedCrossRefGoogle Scholar
  26. 26.
    Forche A, May G, Magee PT. (2005) Demonstration of loss of heterozygosity by single-nucleotide polymorphism microarray analysis and alterations in strain morphology in Candida albicans strains during infection. Eukaryot Cell 4:156–165.PubMedCrossRefGoogle Scholar
  27. 27.
    Irving JA, Bloodworth L, Bown NP, et al. (2005) Loss of heterozygosity in childhood acute lymphoblastic leukemia detected by genome-wide microarray single nucleotide polymorphism analysis. Cancer Res 65:3053–3058.PubMedCrossRefGoogle Scholar
  28. 28.
    Jacobs S, Thompson ER, Nannya Y, et al. (2007) Genome-wide, high-resolution detection of copy number, loss of heterozygosity, and genotypes from formalin-fixed, paraffin-embedded tumor tissue using microarrays. Cancer Res 67:2544–2551.PubMedCrossRefGoogle Scholar
  29. 29.
    Oostenbrug LE, Nolte IM, Oosterom E, et al. (2006) CARD15 in inflammatory bowel disease and Crohn’s disease phenotypes: an association study and pooled analysis. Dig Liver Dis 38:834–845.PubMedCrossRefGoogle Scholar
  30. 30.
    Duerr RH, Taylor KD, Brant SR, et al. (2006) A genome-wide association study identifies IL23R as an inflammatory bowel disease gene. Science 314:1461–1463.PubMedCrossRefGoogle Scholar
  31. 31.
    Frazer KA, Ballinger DG, Cox DR, et al. (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature 449:851–861.PubMedCrossRefGoogle Scholar
  32. 32.
    Everberg H, Clough J, Henderson P, Jergil B, Tjerneld F, Ramirez IB. (2006) J Chromatogr A 1118:244–252. http://www.hapmap.org/ PubMedCrossRefGoogle Scholar
  33. 33.
    Birney E, Stamatoyannopoulos JA, Dutta A, et al. (2007) Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447:799–816.PubMedCrossRefGoogle Scholar
  34. 34.
    Fire A, Xu S, Montgomery MK, et al. (1998) Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature 391:806–811.PubMedCrossRefGoogle Scholar
  35. 35.
    Rossi JJ. (2004) Medicine: a cholesterol connection in RNAi. Nature 432:155–156.PubMedCrossRefGoogle Scholar
  36. 36.
    Soutschek J, Akinc A, Bramlage B, et al. (2004) Therapeutic silencing of an endogenous gene by systemic administration of modified siRNAs. Nature 432: 173–178.PubMedCrossRefGoogle Scholar
  37. 37.
    Hutchinson E. (2006) Expression profiling: Small but influential. Nat Rev Cancer 6:345.CrossRefGoogle Scholar
  38. 38.
    Yanaihara N, Caplen N, Bowman E, et al. (2006) Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 9:189–198.PubMedCrossRefGoogle Scholar
  39. 39.
    Meltzer PS. (2005) Cancer genomics: small RNAs with big impacts. Nature 435:745–746.PubMedCrossRefGoogle Scholar
  40. 40.
    Blenkiron C, Goldstein LD, Thorne NP, et al. (2007) MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype. Genome Biol 8:R214.PubMedCrossRefGoogle Scholar
  41. 41.
    Pruijn GJ. (2006) The RNA interference pathway: a new target for autoimmunity. Arthritis Res Ther 8:110.PubMedCrossRefGoogle Scholar
  42. 42.
    Miller VM, Gouvion CM, Davidson BL, et al. (2004) Targeting Alzheimer’s disease genes with RNA interference: an efficient strategy for silencing mutant alleles. Nucleic Acids Res 32:661–668.PubMedCrossRefGoogle Scholar
  43. 43.
    Knutsen T, Gobu V, Knaus R, et al. (2005) The interactive online SKY/M-FISH & CGH database and the Entrez cancer chromosomes search database: linkage of chromosomal aberrations with the genome sequence. Genes Chromosomes Cancer 44:52–64.PubMedCrossRefGoogle Scholar
  44. 44.
    Hardison RC. (2003) Comparative genomics. PLoS Biol 1:E58.PubMedCrossRefGoogle Scholar
  45. 45.
    Bergman CM, Pfeiffer BD, Rincon-Limas DE, et al. (2002). Assessing the impact of comparative genomic sequence data on the functional annotation of the Drosophila genome. Genome Biol 3:RESEARCH0086.Google Scholar
  46. 46.
    Fermin D, Allen BB, Blackwell TW, Menon R, Adamski M, Xu Y, Ulintz P, Omenn GS, States DJ. (2006) Genome Biol 7:R35.PubMedCrossRefGoogle Scholar
  47. 47.
    Sabbioni G, Sepai O, Norppa H, et al. (2007) Comparison of biomarkers in workers exposed to 2,4,6-trinitrotoluene. Biomarkers 12:21–37.PubMedCrossRefGoogle Scholar
  48. 48.
    Lakhan SE. (2006) Schizophrenia proteomics: biomarkers on the path to laboratory medicine ? Diagn Pathol 1:11.PubMedCrossRefGoogle Scholar
  49. 49.
    Hunter DJ, Kraft P, Jacobs KB, et al. (2007) A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet 39:870–874.PubMedCrossRefGoogle Scholar
  50. 50.
    Easton DF, Pooley KA, Dunning AM, et al. (2007) Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 447:1087–1093.PubMedCrossRefGoogle Scholar
  51. 51.
    Cox J, Mann M. (2007) Is proteomics the new genomics ? Cell 130:395–398.PubMedCrossRefGoogle Scholar
  52. 52.
    Yates JR, 3rd, Gilchrist A, Howell KE, et al. (2005) Proteomics of organelles and large cellular structures. Nat Rev Mol Cell Biol 6: 702–714.PubMedCrossRefGoogle Scholar
  53. 53.
    Zheng J, Gao X, Beretta L, He F. (2006) The Human Liver Proteome Project (HLPP) workshop during the 4th HUPO World Congress. Proteomics 6:1716–1718.PubMedCrossRefGoogle Scholar
  54. 54.
    Hamacher M, Stephan C, Bluggel M, et al. (2006) The HUPO Brain Proteome Project jamboree: centralised summary of the pilot studies. Proteomics 6:1719–1721.PubMedCrossRefGoogle Scholar
  55. 55.
    Gorg A, Obermaier C, Boguth G, et al. (2000) The current state of two-dimensional electrophoresis with immobilized pH gradients. Electrophoresis 21:1037–1053.PubMedCrossRefGoogle Scholar
  56. 56.
    Pelzing M, Neususs C. (2005) Separation techniques hyphenated to electrospray-tandem mass spectrometry in proteomics: capillary electrophoresis versus nanoliquid chromatography. Electrophoresis 26:2717–2728.PubMedCrossRefGoogle Scholar
  57. 57.
    Seet BT, Dikic I, Zhou MM, et al. (2006) Reading protein modifications with interaction domains. Nat Rev Mol Cell Biol 7:473–483.PubMedCrossRefGoogle Scholar
  58. 58.
    Aebersold R, Mann M. (2003) Mass spectrometry-based proteomics. Nature 422:198–207.PubMedCrossRefGoogle Scholar
  59. 59.
    Hattan SJ, Parker KC. (2006) Methodology utilizing MS signal intensity and LC retention time for quantitative analysis and precursor ion selection in proteomic LC-MALDI analyses. Anal Chem 78:7986–7996.PubMedCrossRefGoogle Scholar
  60. 60.
    Wan Y, Yang A, Chen T. (2006) Anal Chem 78:432–437. http://us.expasy.org/tools/ PubMedCrossRefGoogle Scholar
  61. 61.
    Beck HC, Nielsen EC, Matthiesen R, et al. (2006) Quantitative proteomic analysis of post-translational modifications of human histones. Mol Cell Proteomics 5:1314–1325.PubMedCrossRefGoogle Scholar
  62. 62.
    Listgarten J, Emili A. (2005) Statistical and computational methods for comparative proteomic profiling using liquid chromatography-tandem mass spectrometry. Mol Cell Proteomics 4:419–434.PubMedCrossRefGoogle Scholar
  63. 63.
    Matthiesen R, Trelle MB, Hojrup P, et al. (2005) VEMS 3.0: algorithms and computational tools for tandem mass spectrometry based identification of post-translational modifications in proteins. J Proteome Res 4:2338–2347.PubMedCrossRefGoogle Scholar
  64. 64.
    Tokheim AM, Martin BL. (2006) Proteins 64:28–33. http://msquant.sourceforge.net/ PubMedCrossRefGoogle Scholar
  65. 65.
    MacCoss MJ, Wu CC, Liu H, et al. (2003) A correlation algorithm for the automated quantitative analysis of shotgun proteomics data. Anal Chem 75:6912–6921.PubMedCrossRefGoogle Scholar
  66. 66.
    Venable JD, Dong MQ, Wohlschlegel J, et al. (2004) Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat Methods 1:39–45.PubMedCrossRefGoogle Scholar
  67. 67.
    Matthiesen R. (2007) Methods, algorithms and tools in computational proteomics: a practical point of view. Proteomics 7:2815–2832.PubMedCrossRefGoogle Scholar
  68. 68.
    Mueller LN, Brusniak MY, Mani DR, et al. (2008) An Assessment of Software Solutions for the Analysis of Mass Spectrometry Based Quantitative Proteomics Data. J Proteome Res 7:51–61.PubMedCrossRefGoogle Scholar
  69. 69.
    Doyle HA, Mamula MJ. (2005) Posttranslational modifications of self-antigens. Ann N Y Acad Sci 1050:1–9.PubMedCrossRefGoogle Scholar
  70. 70.
    Yuan C, Ravi R, Murphy AM. (2005) Discovery of disease-induced post-translational modifications in cardiac contractile proteins. Curr Opin Mol Ther 7:234–239.PubMedGoogle Scholar
  71. 71.
    Biroccio A, Del Boccio P, Panella M, et al. (2006) Differential post-translational modifications of transthyretin in Alzheimer’s disease: a study of the cerebral spinal fluid. Proteomics 6:2305–2313.PubMedCrossRefGoogle Scholar
  72. 72.
    Kim JK, Mastronardi FG, Wood DD, et al. (2003) Multiple sclerosis: an important role for post-translational modifications of myelin basic protein in pathogenesis. Mol Cell Proteomics 2:453–462.PubMedGoogle Scholar
  73. 73.
    Anderton SM. (2004) Post-translational modifications of self antigens: implications for autoimmunity. Curr Opin Immunol 16:753–758.PubMedCrossRefGoogle Scholar
  74. 74.
    Eastman RT, Buckner FS, Yokoyama K, et al. (2006) Thematic review series: lipid posttranslational modifications. Fighting parasitic disease by blocking protein farnesylation. J Lipid Res 47:233–240.PubMedCrossRefGoogle Scholar
  75. 75.
    Lamerz J, Selle H, Scapozza L, et al. (2005) Correlation-associated peptide networks of human cerebrospinal fluid. Proteomics 5:2789–2798.PubMedCrossRefGoogle Scholar
  76. 76.
    Tanner S, Payne SH, Dasari S, et al. (2008) Accurate Annotation of Peptide Modifications through Unrestrictive Database Search. J Proteome Res 7:170–181.PubMedCrossRefGoogle Scholar
  77. 77.
    Kim S, Na S, Sim JW, et al. (2006) MODi: a powerful and convenient web server for identifying multiple post-translational peptide modifications from tandem mass spectra. Nucleic Acids Res 34:W258–W263.PubMedCrossRefGoogle Scholar
  78. 78.
    Zamdborg L, LeDuc RD, Glowacz KJ, et al. (2007) ProSight PTM 2.0: improved protein identification and characterization for top down mass spectrometry. Nucleic Acids Res 35:W701–W706.PubMedCrossRefGoogle Scholar
  79. 79.
    Griffiths J. (2007) The way of array. Anal Chem 79:8833.CrossRefGoogle Scholar
  80. 80.
    Lv LL, Liu BC. (2007) High-throughput antibody microarrays for quantitative proteomic analysis. Expert Rev Proteomics 4:505–513.PubMedCrossRefGoogle Scholar
  81. 81.
    Espina V, Wulfkuhle JD, Calvert VS, et al. (2007) Reverse phase protein microarrays for monitoring biological responses. Methods Mol Biol 383:321–336.PubMedCrossRefGoogle Scholar
  82. 82.
    LaBaer J, Ramachandran N. (2005) Protein microarrays as tools for functional proteomics. Curr Opin Chem Biol 9:14–19.PubMedCrossRefGoogle Scholar
  83. 83.
    Joos TO, Schrenk M, Hopfl P, et al. (2000) A microarray enzyme-linked immunosorbent assay for autoimmune diagnostics. Electrophoresis 21:2641–2650.PubMedCrossRefGoogle Scholar
  84. 84.
    Robinson WH, DiGennaro C, Hueber W, et al. (2002) Autoantigen microarrays for multiplex characterization of autoantibody responses. Nat Med 8:295–301.PubMedCrossRefGoogle Scholar
  85. 85.
    Balboni I, Chan SM, Kattah M, et al. (2006) Multiplexed protein array platforms for analysis of autoimmune diseases. Annu Rev Immunol 24:391–418.PubMedCrossRefGoogle Scholar
  86. 86.
    Ramachandran N, Hainsworth E, Bhullar B, et al. (2004) Self-assembling protein microarrays. Science 305:86–90.PubMedCrossRefGoogle Scholar
  87. 87.
    Taussig MJ, Stoevesandt O, Borrebaeck CA, et al. (2007) ProteomeBinders: planning a European resource of affinity reagents for analysis of the human proteome. Nat Methods 4:13–17.PubMedCrossRefGoogle Scholar
  88. 88.
    Nolan JP, Sklar LA. (2002) Suspension array technology: evolution of the flat-array paradigm. Trends Biotechnol 20:9–12.PubMedCrossRefGoogle Scholar
  89. 89.
    Wang L, Cole KD, Peterson A, et al. (2007) Monoclonal antibody selection for interleukin-4 quantification using suspension arrays and forward-phase protein microarrays. J Proteome Res 6:4720–4727.PubMedCrossRefGoogle Scholar
  90. 90.
    McLaughlin T, Siepen JA, Selley J, Lynch JA, Lau KW, Yin H, Gaskell SJ, Hubbard SJ. (2006) Nucleic Acids Res 34:D649–D654. http://www.eupa.org/ PubMedCrossRefGoogle Scholar
  91. 91.
    Wishart DS, Tzur D, Knox C, et al. (2007) HMDB: the Human Metabolome Database. Nucleic Acids Res 35:D521–D526.PubMedCrossRefGoogle Scholar
  92. 92.
    Salek RM, Maguire ML, Bentley E, et al. (2007) A metabolomic comparison of urinary changes in type 2 diabetes in mouse, rat, and human. Physiol Genomics 29:99–108.PubMedGoogle Scholar
  93. 93.
    Vangala S, Tonelli A. (2007) Biomarkers, metabonomics, and drug development: can inborn errors of metabolism help in understanding drug toxicity? AAPS J 9:E284–E297.PubMedCrossRefGoogle Scholar
  94. 94.
    Scriver CR. (2007) The PAH gene, phenylketonuria, and a paradigm shift. Hum Mutat 28:831–845.PubMedCrossRefGoogle Scholar
  95. 95.
    Peters T, Thaete C, Wolf S, Popp A, et al. (2003) A mouse model for cystinuria type I. Hum Mol Genet 12:2109–2120.PubMedCrossRefGoogle Scholar
  96. 96.
    Weiss KM. (1996) Variation in the human genome, Introduction. Ciba Found Symp 197:1–5.PubMedGoogle Scholar
  97. 97.
    Scriver CR, Byck S, Prevost L, et al. (1996) The phenylalanine hydroxylase locus: a marker for the history of phenylketonuria and human genetic diversity. PAH Mutation Analysis Consortium. Ciba Found Symp 197:73–90; discussion 90–66.PubMedGoogle Scholar
  98. 98.
    Botstein D, Risch N. (2003) Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nat Genet 33(Suppl):228–237.PubMedCrossRefGoogle Scholar
  99. 99.
    Dettmer K, Hammock BD. (2004) Metabolomics–a new exciting field within the “omics” sciences. Environ Health Perspect 112:A396–A397.PubMedGoogle Scholar
  100. 100.
    Hollywood K, Brison DR, Goodacre R. (2006) Metabolomics: current technologies and future trends. Proteomics 6:4716–4723.PubMedCrossRefGoogle Scholar
  101. 101.
    Baumgartner C, Baumgartner D. (2006) Biomarker discovery, disease classification, and similarity query processing on high-throughput MS/MS data of inborn errors of metabolism. J Biomol Screen 11:90–99.PubMedCrossRefGoogle Scholar
  102. 102.
    Dettmer K, Aronov PA, Hammock BD. (2007) Mass spectrometry-based metabolomics. Mass Spectrom Rev 26:51–78.PubMedCrossRefGoogle Scholar
  103. 103.
    Griffin JL, Scott J, Nicholson JK. (2007) The influence of pharmacogenetics on fatty liver disease in the wistar and kyoto rats: a combined transcriptomic and metabonomic study. J Proteome Res 6:54–61.PubMedCrossRefGoogle Scholar
  104. 104.
    Griffin JL, Bonney SA, Mann C, et al. (2004) An integrated reverse functional genomic and metabolic approach to understanding orotic acid-induced fatty liver. Physiol Genomics 17:140–149.PubMedCrossRefGoogle Scholar
  105. 105.
    Kanehisa M, Goto S, Kawashima S, et al. (2004) The KEGG resource for deciphering the genome. Nucleic Acids Res 32:D277–D280.PubMedCrossRefGoogle Scholar
  106. 106.
    Krummenacker M, Paley S, Mueller L, et al. (2005) Querying and computing with BioCyc databases. Bioinformatics 21:3454–3455.PubMedCrossRefGoogle Scholar
  107. 107.
    Joshi-Tope G, Gillespie M, Vastrik I, et al. (2005) Reactome: a knowledgebase of biological pathways. Nucleic Acids Res 33:D428–D432.PubMedCrossRefGoogle Scholar
  108. 108.
    McKusick VA. (2007) Mendelian Inheritance in Man and its online version, OMIM. Am J Hum Genet 80:588–604.PubMedCrossRefGoogle Scholar
  109. 109.
    Steely HT, Dillow GW, Bian L, Grundstad J, Braun TA, Casavant TL, McCartney MD, Clark AF. (2006) Mol Vis 12:372–383. http://www.hupo.org/overview/glossary/ PubMedGoogle Scholar
  110. 110.
    Cambien F, Tiret L. (2007) Genetics of cardiovascular diseases: from single mutations to the whole genome. Circulation 116:1714–1724.PubMedCrossRefGoogle Scholar
  111. 111.
    Kingsmore SF, Lindquist IE, Mudge J, et al. (2007) Genome-Wide Association Studies: Progress in Identifying Genetic Biomarkers in Common, Complex Diseases. Biomarker Insights 2:283–292.PubMedGoogle Scholar
  112. 112.
    Srinivas PR, Verma M, Zhao Y, et al. (2002) Proteomics for cancer biomarker discovery. Clin Chem 48:1160–1169.PubMedGoogle Scholar
  113. 113.
    Meyer HE, Stuhler K. (2007) High-performance Proteomics as a Tool in Biomarker Discovery. Proteomics 7(Suppl 1):18–26.PubMedCrossRefGoogle Scholar
  114. 114.
    Vosseller K. (2007) Proteomics of Alzheimer’s disease: Unveiling protein dysregulation in complex neuronal systems. Proteomics Clin Appl 1:1351–1361.CrossRefGoogle Scholar
  115. 115.
    Iorio MV, Visone R, Di Leva G, et al. (2007) MicroRNA signatures in human ovarian cancer. Cancer Res 67:8699–8707.PubMedCrossRefGoogle Scholar
  116. 116.
    Goodenowe DB, Cook LL, Liu J, et al. (2007) Peripheral ethanolamine plasmalogen deficiency: a logical causative factor in Alzheimer’s disease and dementia. J Lipid Res 48:2485–2498.PubMedCrossRefGoogle Scholar
  117. 117.
    Martin R, Bielekova B, Hohlfeld R, et al. (2006) Biomarkers in multiple sclerosis. Dis Markers 22:183–185.PubMedGoogle Scholar
  118. 118.
    Weinshenker BG, Wingerchuk DM, Pittock SJ, et al. (2006) NMO-IgG: a specific biomarker for neuromyelitis optica. Dis Markers 22:197–206.PubMedGoogle Scholar
  119. 119.
    Berger T, Reindl M. (2006) Biomarkers in multiple sclerosis: role of antibodies. Dis Markers 22:207–212.PubMedGoogle Scholar
  120. 120.
    O’Connor KC, Roy SM, Becker CH, et al. (2006) Comprehensive phenotyping in multiple sclerosis: discovery based proteomics and the current understanding of putative biomarkers. Dis Markers 22:213–225.PubMedGoogle Scholar
  121. 121.
    Bhattacharyya S, Epstein J, Suva LJ. (2006) Biomarkers that discriminate multiple myeloma patients with or without skeletal involvement detected using SELDI-TOF mass spectrometry and statistical and machine learning tools. Dis Markers 22:245–255.PubMedGoogle Scholar
  122. 122.
    Hoshida Y, Brunet JP, Tamayo P, et al. (2007) Subclass mapping: identifying common subtypes in independent disease data sets. PLoS ONE 2:e1195.PubMedCrossRefGoogle Scholar
  123. 123.
    Liu JJ, Cutler G, Li W, et al. (2005) Multiclass cancer classification and biomarker discovery using GA-based algorithms. Bioinformatics 21:2691–2697.PubMedCrossRefGoogle Scholar
  124. 124.
    Harris L, Fritsche H, Mennel R, et al. (2007) American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J Clin Oncol 25:5287–5312.PubMedCrossRefGoogle Scholar
  125. 125.
    van ’t Veer LJ, Dai H, van de Vijver MJ, et al. (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415:530–536.PubMedCrossRefGoogle Scholar
  126. 126.
    El-Rehim DMA, Ball G, Pinder SE, et al. (2005) High-throughput protein expression analysis using tissue microarray technology of a large well-characterised series identifies biologically distinct classes of breast cancer confirming recent cDNA expression analyses. Int J Cancer 116:340–350.CrossRefGoogle Scholar
  127. 127.
    Makretsov NA, Huntsman DG, Nielsen TO, et al. (2004) Hierarchical clustering analysis of tissue microarray immunostaining data identifies prognostically significant groups of breast carcinoma. Clin Cancer Res 10:6143–6151.PubMedCrossRefGoogle Scholar
  128. 128.
    Nielsen TO, Hsu FD, Jensen K, et al. (2004) Immunohistochemical and clinical characterization of the basal-like subtype of invasive breast carcinoma. Clin Cancer Res 10:5367–5374.PubMedCrossRefGoogle Scholar
  129. 129.
    Levenson D. (2007) Gene Expression Profile Tests for Breast Cancer Recurrence. Clin Lab News 33:4–5.Google Scholar
  130. 130.
    Sotiriou C, Piccart MJ. (2007) Taking gene-expression profiling to the clinic: when will molecular signatures become relevant to patient care? Nat Rev Cancer 7:545–553.PubMedCrossRefGoogle Scholar
  131. 131.
    McCabe N, Turner NC, Lord CJ, et al. (2006) Deficiency in the repair of DNA damage by homologous recombination and sensitivity to poly(ADP-ribose) polymerase inhibition. Cancer Res 66:8109–8115.PubMedCrossRefGoogle Scholar
  132. 132.
    O’Connor M. (2006) Proteomics Success Story. Novel Biomarkers for DNA Damage Response Pathways: Insights and Applications for Cancer Therapy. Proteomics 6:69–71.PubMedCrossRefGoogle Scholar
  133. 133.
    Souchelnytskyi S, Lomnytska M, Dubrovska A, et al. (2006) Proteomics Success Story. Towards Early Detection of Breast and Ovarian Cancer: Plasma Proteomics as a Tool to Find Novel Markers. Proteomics 6:65–68.PubMedCrossRefGoogle Scholar
  134. 134.
    Lomnytska M, Dubrovska A, Hellman U, et al. (2006) Increased expression of cSHMT, Tbx3 and utrophin in plasma of ovarian and breast cancer patients. Int J Cancer 118:412–421.PubMedCrossRefGoogle Scholar
  135. 135.
    Brenner DE, Normolle DP. (2007) Biomarkers for cancer risk, early detection, and prognosis: the validation conundrum. Cancer Epidemiol Biomarkers Prev 16:1918–1920.PubMedCrossRefGoogle Scholar
  136. 136.
    Coombes KR, Morris JS, Hu J, et al. (2005) Serum proteomics profiling–a young technology begins to mature. Nat Biotechnol 23:291–292.PubMedCrossRefGoogle Scholar
  137. 137.
    Wang SJ, Cohen N, Katz DA, et al. (2006) Retrospective validation of genomic biomarkers– what are the questions, challenges and strategies for developing useful relationships to clinical outcomes– workshop summary. Pharmacogenomics J 6:82–88.PubMedCrossRefGoogle Scholar
  138. 138.
    Wang MC, Valenzuela LA, Murphy GP, et al. (1979) Purification of a human prostate specific antigen. Invest Urol 17:159–163.PubMedGoogle Scholar
  139. 139.
    Papsidero LD, Wang MC, Valenzuela LA, et al. (1980) A prostate antigen in sera of prostatic cancer patients. Cancer Res 40:2428–2432.PubMedGoogle Scholar
  140. 140.
    Diamandis EP. (2000) Prostate-specific antigen: a cancer fighter and a valuable messenger? Clin Chem 46:896–900.PubMedGoogle Scholar
  141. 141.
    Wang MC, Valenzuela LA, Murphy GP, et al. (2002) Purification of a human prostate specific antigen. 1979. J Urol 167:960–964; discussion 64–65.PubMedCrossRefGoogle Scholar
  142. 142.
    Liu FC, Chang DM, Lai JH, et al. (2007) Autoimmune hepatitis with raised alpha-fetoprotein level as the presenting symptoms of systemic lupus erythematosus: a case report. Rheumatol Int 27:489–491.PubMedCrossRefGoogle Scholar
  143. 143.
    Supriatna Y, Kishimoto T, Furuya M, et al. (2007) Expression of liver-enriched nuclear factors and their isoforms in alpha-fetoprotein-producing gastric carcinoma cells. Exp Mol Pathol 82:316–321.PubMedCrossRefGoogle Scholar
  144. 144.
    Campana D, Nori F, Piscitelli L, et al. (2007) Chromogranin A: is it a useful marker of neuroendocrine tumors? J Clin Oncol 25:1967–1973.PubMedCrossRefGoogle Scholar
  145. 145.
    Zatelli MC, Torta M, Leon A, et al. (2007) Chromogranin A as a marker of neuroendocrine neoplasia: an Italian Multicenter Study. Endocr Relat Cancer 14:473–482.PubMedCrossRefGoogle Scholar
  146. 146.
    Bradley DA, Redman BG. (2007) The times they are a-changin’ (Bob Dylan, 1964). Cancer 110:2366–2369.PubMedCrossRefGoogle Scholar
  147. 147.
    Ma Q, Abel K, Sripichai O, et al. (2007) Beta-globin gene cluster polymorphisms are strongly associated with severity of HbE/beta(0)-thalassemia. Clin Genet 72:497–505.PubMedCrossRefGoogle Scholar
  148. 148.
    Erlich PM, Lunetta KL, Cupples LA, et al. (2006) Polymorphisms in the PON gene cluster are associated with Alzheimer disease. Hum Mol Genet 15:77–85.PubMedCrossRefGoogle Scholar
  149. 149.
    Selwood SP, Parvathy S, Cordell B, et al. (2007) Gene expression profile of the PDAPP mouse model for Alzheimer’s disease with and without Apolipoprotein E. Neurobiol Aging 30:574–90.Google Scholar
  150. 150.
    Prentice H, Webster KA. (2004) Genomic and proteomic profiles of heart disease. Trends Cardiovasc Med 14:282–288.PubMedCrossRefGoogle Scholar
  151. 151.
    Sanchez-Carbayo M, Socci ND, Richstone L, et al. (2007) Genomic and proteomic profiles reveal the association of gelsolin to TP53 status and bladder cancer progression. Am J Pathol 171:1650–1658.PubMedCrossRefGoogle Scholar
  152. 152.
    McRedmond JP, Park SD, Reilly DF, et al. (2004) Integration of proteomics and genomics in platelets: a profile of platelet proteins and platelet-specific genes. Mol Cell Proteomics 3:133–144.PubMedGoogle Scholar
  153. 153.
    Ippolito JE, Xu J, Jain S, et al. (2005) An integrated functional genomics and metabolomics approach for defining poor prognosis in human neuroendocrine cancers. Proc Natl Acad Sci USA 102:9901–9906.PubMedCrossRefGoogle Scholar
  154. 154.
    Mootha VK, Lepage P, Miller K, et al. (2003) Identification of a gene causing human cytochrome c oxidase deficiency by integrative genomics. Proc Natl Acad Sci USA 100:605–610.PubMedCrossRefGoogle Scholar
  155. 155.
    Shaham O, Wei R, Wang TJ, et al. (2008) Metabolic profiling of the human response to a glucose challenge reveals distinct axes of insulin sensitivity. Mol Syst Biol 4:214.PubMedCrossRefGoogle Scholar
  156. 156.
    Pagliarini DJ, Calvo SE, Chang B, et al. (2008) A mitochondrial protein compendium elucidates complex I disease biology. Cell 134:112–123.PubMedCrossRefGoogle Scholar
  157. 157.
    Perroud B, Lee J, Valkova N, et al. (2006) Pathway analysis of kidney cancer using proteomics and metabolic profiling. Mol Cancer 5:64.PubMedCrossRefGoogle Scholar
  158. 158.
    Alimonti A, Ristori G, Giubilei F, et al. (2007) Serum chemical elements and oxidative status in Alzheimer’s disease, Parkinson disease and multiple sclerosis. Neurotoxicology 28:450–456.PubMedCrossRefGoogle Scholar
  159. 159.
    Pai SI, Lin YY, Macaes B, et al. (2006) Prospects of RNA interference therapy for cancer. Gene Ther 13:464–477.PubMedCrossRefGoogle Scholar
  160. 160.
    Yano H, Kuroda S. (2008) Introduction of the disulfide proteome: application of a technique for the analysis of plant storage proteins as well as allergens. J Proteome Res 7:3071–3079.PubMedCrossRefGoogle Scholar
  161. 161.
    Griffin JL, Vidal-Puig A. (2008) Current challenges in metabolomics for diabetes research: a vital functional genomic tool or just a ploy for gaining funding? Physiol Genomics 34:1–5.PubMedCrossRefGoogle Scholar
  162. 162.
    Brindle JT, Antti H, Holmes E, et al. (2002) Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nat Med 8:1439–1444.PubMedCrossRefGoogle Scholar
  163. 163.
    Ringeissen S, Connor SC, Brown HR, et al. (2003) Potential urinary and plasma biomarkers of peroxisome proliferation in the rat: identification of N-methylnicotinamide and N-methyl-4-pyridone-3-carboxamide by 1H nuclear magnetic resonance and high performance liquid chromatography. Biomarkers 8:240–271.PubMedCrossRefGoogle Scholar
  164. 164.
    Griffin JL. (2006) Understanding mouse models of disease through metabolomics. Curr Opin Chem Biol 10:309–315.PubMedCrossRefGoogle Scholar
  165. 165.
    Saito Y, Yokota T, Mitani T, et al. (2005) Transgenic small interfering RNA halts amyotrophic lateral sclerosis in a mouse model. J Biol Chem 280:42826–42830.PubMedCrossRefGoogle Scholar
  166. 166.
    Wu F, Dassopoulos T, Cope L, et al. (2007) Genome-wide gene expression differences in Crohn’s disease and ulcerative colitis from endoscopic pinch biopsies: insights into distinctive pathogenesis. Inflamm Bowel Dis 13:807–821.PubMedCrossRefGoogle Scholar
  167. 167.
    The Welcome Trust Case Control Consortium. (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447:661–678.Google Scholar
  168. 168.
    Schadt EE, Molony C, Chudin E, et al. (2008) Mapping the genetic architecture of gene expression in human liver. PLoS Biol 6:e107.PubMedCrossRefGoogle Scholar
  169. 169.
    Kader HA, Tchernev VT, Satyaraj E, et al. (2005) Protein microarray analysis of disease activity in pediatric inflammatory bowel disease demonstrates elevated serum PLGF, IL-7, TGF-beta1, and IL-12p40 levels in Crohn’s disease and ulcerative colitis patients in remission versus active disease. Am J Gastroenterol 100:414–423.PubMedCrossRefGoogle Scholar
  170. 170.
    Bogdanov M, Matson WR, Wang L, et al. (2008) Metabolomic profiling to develop blood biomarkers for Parkinson’s disease. Brain 131:389–396.PubMedCrossRefGoogle Scholar
  171. 171.
    Bergheanu SC, Reijmers T, Zwinderman AH, et al. (2008) Lipidomic approach to evaluate rosuvastatin and atorvastatin at various dosages: investigating differential effects among statins. Curr Med Res Opin 24:2477–2487.Google Scholar
  172. 172.
    Leiserowitz GS, Lebrilla C, Miyamoto S, et al. (2008) Glycomics analysis of serum: a potential new biomarker for ovarian cancer? Int J Gynecol Cancer 18:470–475.PubMedCrossRefGoogle Scholar

Copyright information

© Humana Press, a part of Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Ewa Gubb
    • 1
  • Rune Matthiesen
    • 2
  1. 1.Bioinformatics, Parque Technológico de BizkaiaDerioSpain
  2. 2.Instituto de Patologia e Imunologia Molecular da Universidad do Porto – IPATIMUPPortoPortugal

Personalised recommendations